Slope stability of homogenous earth dams is a very important design analysis to consider since earth dams are prone to failure on slopes due some factors such as material properties and the design slope of the downstream side. SLOPE/W simulation using the method of slices was utilized to determine the effect of the input parameters in a Mohr-Coulomb analysis including the unit weight, cohesion, and angle of internal friction (Ø) of the dam material to be used. Hydraulic structure designers encounter difficulties in analyzing the best design which include the proper slope due to the complexity of the required input parameters and other factors affecting the stability of the dam. It is the main objective of this paper to provide a prediction model of the slope stability (SS) of homogenous earth dams using neural network (NN) particle swarm optimization (PSO) model. The model output will be of great contribution to the designers in investigating the proper earth material and slope design to consider both the structural integrity and economic aspect of the structure. There were seven (7) slopes and 14 earth materials analyzed in the study. The particle swarm optimization technique was implemented and utilized to train and optimize the initial neural network model. The material parameters such as unit weight of the soil (kN/m3), cohesion (kPa), angle of internal friction Ø (degrees), and slope were utilized as the input variables in the study while the factor of safety (FOS) values was used as the output variable. The developed NN-PSO model has an R (all) value of 0.99752, while the MSE of the developed NN-PSO model is 0.0021844. The values of R and MSE are close to the ideal values of 1.0 and 0 respectively, showing further that the model is satisfactory. Using the Garson’s algorithm, it was observed that the angle of friction is the most significant parameter to the FOS.

Urban systems have a central role in the transition toward circular economy. Systematic analysis of drivers is needed because of the complex interplay of social, economic, and political factors. Such analysis requires a good understanding of direct and indirect influences on urban circularity. Because of the presence of indirect influences, visualizing the causal networks is necessary for systematic analysis. Existing methods for formulating causal network maps (CNMs) rely on subjective approaches which inhibit robust assessment. The generation of robust CNMs can provide more accurate representation of direct and indirect influences. Therefore, this study generates a robust CNM for drivers of urban circular economy through a hybrid decision-making and trial laboratory-fuzzy cognitive map (DEMATEL-FCM) framework. DEMATEL is used for building the initial structure of the network map. The network is then trained using FCM with data obtained from the Sustainable Cities Index. A 70:30 training–testing ratio is used to partition the training and testing datasets. The trained CNM has 92.75% accuracy during training and 96.77% accuracy during testing. The trained CNM provides an empirical depiction of driver interrelationships in urban circular economies. It indicates the importance of ‘affordability’ and ‘economic development’ in the network structure. The network yields significant insights for the development of city-level plans and policies to stimulate a transition to a more circular economy. Data-driven visualization of interactions among drivers give stakeholders insights on the most effective measures to implement.

HWYL (meaning “a stirring feeling of emotional motivation and energy”) is a 3D isometric puzzle adventure game for Android devices. The whole game revolves around the adventures of Thomas as he unknowingly helps the mayor and the townspeople in solving environmental problems through playing meaningful mini games. The mini games comprise of different casual games that focus on teaching the players about the major environmental issues such as ozone depletion, and disposal of wastes. The interactive learning experience educates players how to mitigate environmental problems. The game garnered very satisfactory results from the play testers, proving that the game has been successful in promoting environmental awareness through edutainment, and the game as a system works as intended with compliance to software quality factors.

Sentiment analysis has been extensively researched for the purpose of analyzing qualitative data using a computation technique. However, there are only a few research papers that focus on analyzing sentiments in terms of teachers’ evaluation. Analyzing comments on teacher evaluations can lead to understanding more of what faculty development programs can be provided to improve teachers’ academic performance. Thus, this study presents an opinion-mining system utilizing the Bayesian technique of the Naïve Bayes algorithm. The descriptive research method was used in this study, with a questionnaire serving as the instrument for testing the acceptability of the application. One hundred (100) evaluators were surveyed for the teachers’ evaluations. The application performance attributes are defined using the functionality, usability, reliability, performance and security (FURPS) model. The mean formula was used to analyze the data. The usability and security were evaluated as perfectly acceptable, with a weighted mean of 4.64, and 4.56, respectively. Furthermore, the functionality, reliability, and performance were evaluated at acceptable evaluation ratings with a weighted mean of 4.29, 4.02, and 4.11. The overall quality of the system was given an acceptable rating with a weighted mean of 4.32, indicating that the application provided and managed ratings and comments on individual teachers’ performance.

E-Aid is a study that aims to develop an application based on the convolutional neural network algorithm. The central idea for the creation of E-Aid is to provide a mobile application which offers more advanced capabilities and leads to a strong emergence for the medical health applications in the market. The reliability for the usage of CNN as an algorithm produces positive results which is essential for this study. The researchers trained CNN model that will be used later on during the execution of the CCN algorithm, and this CNN model must be able to identify 4 types of open wounds (laceration, puncture, abrasion and avulsion) and 4 types of skin burns (1st-, 2nd-, 3rd- and 4th-degree burn) and also must be able to classify it whether the wound is infected or not infected. The researchers tested the accuracy of the CNN model before sending to our respondents. The researchers tested the accuracy by getting a random image of open wounds and skin burns in the Internet and run it on the E-Aid app. After the researchers finish testing the accuracy of the app, they distributed the app to their respondents to test furthermore the accuracy and reliability of the app. The researchers’ respondents are composed of 6 medical professionals (doctors/nurses), 5 IT/CS professionals and 14 students (in the field of medicine and computer studies).

Optical character recognition (OCR) is the process of extracting the characters from a digital image. The concept behind OCR is to acquire a text in a video or image formats and extract the characters from that image and present it to the user in an editable format. In this study, a convolutional neural network (CNN) is applied, which is a mathematical representation of the functionality of the human brain, using back-propagation algorithm with test case files of English alphabets and numbers. The purpose of this study is to test systems capable of recognizing vehicle plate number English alphabets and numbers with different fonts, and to be familiar with CNN and digital image processing applied for character recognition. Scientific journals and reports were used to research the relevant information required for the thesis project. The chosen software was then trained and tested with both computer and video output files. The tests revealed that the OCR software can recognize both vehicular plate and computer alphabets and learns to do it better with each iteration. The study shows that although the system needs more training for vehicular plate characters than computerized fonts, and the use of CNN in OCR is of great benefit and allows for quicker and better character recognition.

Flooding is a perennial problem in the Philippines, particularly in its capital city of Manila. Sampaloc is one of the barangays in Manila vulnerable to flooding according to the Flood Risk Map of Metro Manila. The researchers considered España Boulevard as the area of study since it is one of the most flood prone roads in Manila, according to Metro Manila Development Authority (MMDA). The study is focused on the analysis of Rainwater Gardens as an additional flood mitigation in España Boulevard using Storm Water Management Model (SWMM) simulation. Studies have proven that rainwater garden is considered as one of the most effective, simplest, and low-cost methods to address abrupt flooding. Moreover, it is easy to install, maintain and has a lot of advantages such as removing nutrient-based pollutants such as nitrogen and phosphorus, improving air quality, money saving and water conservation and improving environmental aesthetics. The data was collected through online surveys. The gathered data was calibrated and simulated using SWMM. As per the results, the rainwater garden is effective as an additional flood mitigation system since it can reduce the flood depth up to 19.42% and 14.78% for 25-year return period and 50-year return period storm, respectively. The delay of abrupt flooding is beneficial to the residents of flood prone areas. In real life scenario, the 0.15 m difference in flood depth for a 25-year return period storm and 0.17 m difference in flood depth for a 50-year return period storm will serve as longer time for evacuation of the residents when excessive flooding occurs. Moreover, rescuers will have more time to respond to affected areas and save more people.

By early 2020, COVID-19 has caused a global pandemic which led to an enormous number of challenges worldwide in various sectors. The Philippine government has implemented multiple quarantine guidelines and travel restrictions to ensure the people’s health and safety. However, the International Labour Organization projected an initial economic and labor market disruption affecting 11 million workers, or about 25% of the Philippine workforce, due to the pandemic. Therefore, the government, thru the concerned agencies continues to encourage employers to implement alternative work plans such as a work-from-home (WFH) operation in compliance with the established regulations in line with existing laws and policies. In line with the telecommuting concept, various research has already been performed, however, some were regarded inconclusive and require further study. Hence, in this study, a Web application was developed along with an embedded fuzzy model to evaluate the telecommuting capability assessment of employees. The proposed web application with embedded fuzzy model is capable of providing capability assessment using the four main input variables which are also relatively characterized for possible telecommuting cost assessment.

Corrosion is one of the biggest problems of reinforced concrete structures prone to high chloride environments such as ports and harbors. Due to a lack of studies that can support the use of steel fiber reinforced self-compacting concrete, researchers are still in dispute regarding the effect of using steel fibers in chloride-rich environments. This paper explores the use of neural network modeling to precisely predict and further analyze this problem. Twenty-six different mixtures of steel fiber reinforced self-compacting concrete with varying amounts of cement, water-cement ratio, superplasticizer, and steel fiber were used to derive the feed forward back propagation neural network and compared to a derived non-linear model. The derived neural network model with fourteen hidden nodes and tansig as transfer function has an R-squared of 0.949 for the training. The comparison shows that ANN has superior predicting capability compared to non-linear modeling even with a limited number of data. Parametric analysis was performed and found that steel fiber shows improvement in the corrosion resistance of concrete for mixtures with low to moderate water-cement ratio and an opposite behavior for high water-cement ratio. This is due to the presence of voids formed around the surface of the steel fiber due to capillary action. These voids serve as highways for chloride ions.

The Philippines has both private and health care facilities, and as far as better health is central to human happiness and well-being [12]. It also makes an important contribution to economic progress, as healthy populations live longer and happier, are more productive, and save more lives [5]. Amongst factors that influence health status and a country's ability to provide quality health services for its people [15]. The Manila City is one of the established cities in Metro Manila named as the capital of the Philippines. It is situated as the center of everything, with a total population of almost 13.9 million people, a good place for commercial and capital investments since Manila City is near to everything. With the advent of innovation, Philippines climbs in as readiness for technological change; they say that Southeast Asia, the Philippines is the fourth most tech-ready economy [3]. A sound digital ecosystem concept should be applied to preserve a decent approach to improving the health and wellness of each constituent in the City of Manila by ensuring that all Filipinos have access to suitable health care through functional service delivery networks. A crucial first step in ensuring that all Manilenos comprehend the advantages of good health and may refer to the city as "Healthy Manila City" is to involve both the public and private sectors in the city.